Article
Computer Science, Artificial Intelligence
Lijun He, Wenfeng Li, Raymond Chiong, Mehdi Abedi, Yulian Cao, Yu Zhang
Summary: This study presents an EMOJaya algorithm for solving multi-objective job-shop scheduling problems, which achieves higher performance and more high-quality scheduling schemes through the application of grey entropy parallel analysis and opposition-based learning strategies.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Wu Xiuli, Peng Junjian, Xie Zirun, Zhao Ning, Wu Shaomin
Summary: An improved multi-objective optimization algorithm is proposed for solving the flexible job shop scheduling problem with variable batches, which incorporates the idea of inverse scheduling and dynamic feedback batch adjusting method. The algorithm effectively ensures the diversity of Pareto solutions and demonstrates effective performance in solving the scheduling problem.
JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS
(2021)
Article
Computer Science, Artificial Intelligence
Wenwu Han, Qianwang Deng, Guiliang Gong, Like Zhang, Qiang Luo
Summary: This study focuses on a new realistic hybrid flow shop scheduling problem with worker constraint (HFSSPW) and proposes seven multi-objective evolutionary algorithms to solve the problem, incorporating the earliest due date (EDD) rule into the heuristic decoding methods. The computational results demonstrate the excellent performance of the proposed algorithms in terms of makespan objective.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Mathematics
Yuanfei Wei, Zalinda Othman, Kauthar Mohd Daud, Shihong Yin, Qifang Luo, Yongquan Zhou
Summary: In this paper, a hybrid algorithm called EOSMA is proposed to solve the Job Shop Scheduling Problem (JSSP). The algorithm combines the update strategy of Equilibrium Optimizer (EO) with Slime Mould Algorithm (SMA) to achieve a better balance between exploration and exploitation. The addition of Centroid Opposition-based Computation (COBC) improves exploration and exploitation, increases population diversity, enhances convergence speed and accuracy, and prevents falling into local optima. The algorithm also introduces a Sort-Order-Index (SOI)-based coding method and a neighbor search strategy to improve the efficiency of solving JSSP. Experimental results and statistical analysis demonstrate that EOSMA outperforms other competing algorithms.
Article
Computer Science, Artificial Intelligence
Alireza Goli, Ali Ala, Mostafa Hajiaghaei-Keshteli
Summary: This study investigates the energy awareness of non-permutation flow-shop scheduling and lot-sizing problems and proposes a hybrid algorithm to optimize them. The proposed algorithm is validated and evaluated for efficiency using mathematical modeling and meta-heuristic algorithms, showing it can find optimal solutions and outperform other algorithms in terms of time and quality.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Fawu Xie, Lingling Li, Li Li, Yangpeng Huang, Zaixiang He
Summary: This study investigated the lot-streaming job shop scheduling problem (LSJSP) with variable sublots and intermingling setting, which is rarely considered in the literature. A multi-objective mixed-integer linear programming model (MILP) was formulated to achieve a tradeoff between the shortest tardiness and the minimum number of transferred sublots. In order to solve the problem efficiently, a decomposition based multi-objective Jaya algorithm (MOJA/D) was proposed, which combines the Jaya algorithm and decomposition idea.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Energy & Fuels
Yanwei Sang, Jianping Tan
Summary: With the rise of customized product requirements, manufacturing products are facing challenges of diversity and small-batch production. This study introduces a multi-objective flexible job shop scheduling model and optimization method SV-MA, which can improve production efficiency and reduce energy consumption.
Article
Mathematics
Hankun Zhang, Borut Buchmeister, Xueyan Li, Robert Ojstersek
Summary: This paper proposes an Improved Heuristic Kalman Algorithm to solve the dynamic job shop scheduling problem, which shows effective results in experiments with improved convergence rate, robustness, and reasonable running time.
Article
Computer Science, Artificial Intelligence
Hao Wang, Junfu Cheng, Chang Liu, Yuanyuan Zhang, Shunfang Hu, Liangyin Chen
Summary: This research proposes a new dynamic multi-objective flexible job shop scheduling problem and designs a scheduling algorithm based on deep reinforcement learning. Experimental results demonstrate that the algorithm outperforms other methods in terms of performance improvement.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Guohui Zhang, Xixi Lu, Xing Liu, Litao Zhang, Shiwen Wei, Wenqiang Zhang
Summary: This research proposes an effective two-stage algorithm based on convolutional neural network for solving the flexible job shop scheduling problem. The algorithm is used to train the prediction model and evaluate the robustness of scheduling, with the evaluation done through the proposed RMn metric.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xiao-long Chen, Jun-qing Li, Yu Du
Summary: This study focuses on a fuzzy flexible job shop scheduling problem with variable processing speeds and proposes a multi-objective hybrid evolutionary immune algorithm (HEIA) to solve it by optimizing the fuzzy maximum completion time (makespan) and fuzzy total energy consumption simultaneously. The HEIA adopts a two left-shift heuristic based active decoding method to optimize the fuzzy makespan and incorporates two hybrid evolutionary strategies to enhance the exploration ability and exploitation ability. The proposed HEIA is tested on five types of instances to verify its effectiveness.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Yibing Li, Zipeng Yang, Lei Wang, Hongtao Tang, Libo Sun, Shunsheng Guo
Summary: This paper proposes a variable-size batching method and discusses its application in the manufacturing process. By formulating a multi-objective optimization problem and introducing an improved hybrid algorithm, the issue of increased energy consumption and search space of scheduling schemes due to changing batch sizes is addressed. Experimental results demonstrate the effectiveness of this approach.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Xu Liang, Jiabao Chen, Xiaolin Gu, Ming Huang
Summary: This article presents an improved adaptive non-dominated sorting genetic algorithm with elite strategy to tackle the complex flexible job-shop scheduling problem. By introducing a constructive heuristic algorithm and improving the elite strategy, the algorithm achieves faster generation of Pareto optimal solution set for the multi-objective scheduling model.
Article
Mathematics
Anran Zhao, Peng Liu, Xiyu Gao, Guotai Huang, Xiuguang Yang, Yuan Ma, Zheyu Xie, Yunfeng Li
Summary: In this paper, a pure reactive scheduling method is proposed to deal with the uncertainty of new job arrivals in job shop scheduling. The method combines data mining, discrete event simulation, and dispatching rules to assign optimal DRs to each scheduling subperiod, achieving the purpose of locally updating the scheduling strategy and enhancing the overall scheduling effect of the manufacturing system.
Article
Computer Science, Artificial Intelligence
Radhwane Boufellouh, Faycal Belkaid
Summary: This paper investigates the energy-efficient flow shop scheduling problem with various constraints and proposes an enhanced multi-objective ant colony algorithm to solve this problem. Extensive experiments demonstrate the effectiveness of the proposed method and enhancements in obtaining high quality solutions, and showcase the significance of considering transportation speed control, battery management, and AGV idle power consumption.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)